When an agent is compromised, nothing obvious breaks. 

There is no crash, no clear failure signal, no explicit policy violation. The system continues to behave “correctly” while gradually shifting its objective, exploring access boundaries, and moving data through legitimate tool interactions. By the time the deviation becomes visible, the underlying process has already unfolded.

This is where traditional detection approaches fall short. Most existing safeguards are designed to evaluate discrete inputs or outputs, but agent behavior is inherently continuous and stateful. Meaning emerges across multiple steps within planning, tool selection, memory access, and execution, where each individual action may appear valid in isolation.

To reason about these systems, the agent itself must become observable as a dynamic process rather than a sequence of prompts and responses.

In this in-depth masterclass, we introduce a behavioral observability approach for agentic systems. We instrument key transition points in the execution lifecycle, including context construction, tool selection, tool input and output, memory interactions, policy evaluation, and external side effects. These signals are distilled into compact behavioral traces that capture intent, execution structure, and outcomes without requiring full transcript retention.

We then apply lightweight model-based analysis to these traces in real time, identifying patterns such as goal drift, unintended data aggregation, boundary exploration, anomalous tool usage, and irregular action sequencing. These are behaviors that are difficult to capture with static rules or point-in-time validation.